ML-TN-003 — AI at the edge: visual inspection of assembled PCBs for defect detection — Part 3

Info Box Applies to Machine Learning Applies to Machine Learning Technical Notes

Contents

HistoryEdit

Version	Date	Notes
1.0.0	March 2021	First public release

Building the datasetEdit

Defects generation and acquisitionEdit

Anomalies, generation process and numerosity

Anomaly	P&P	Serigraphy	Manual	Numerosity
Missing	✓			21
Manhattan			✓	4
Shift x-axis	✓			51
Shift y-axis	✓			58
Shift&Rotation (shift x+z-axes)	✓			57
Rotation (z-axis)	✓			55
Under soldering		✓		All
Over soldering		✓		All

Class subdivision and labellingEdit

Soldering regions extractionEdit

Data augmentation with image synthesisEdit

Generative adversarial networksEdit

Progressive GAN implementationEdit

ProGAN: training progression

ProGAN: growing progression of the model during training

Results validationEdit

Class	Synth image	Resolution (pixel)	Google Colab (min)	AWS SageMaker (min)
missing	full	512 × 512	~480	~410
	upper/lower	256 × 256	~435	~310
tombstoning	full	512 × 512	~460	~390
	upper/lower	256 × 256	~420	~300

Synthesized images for missing class

Synthesized images for tombstoning class

T-SNE algorithm results for missing class synthesized images

512 × 512 resolution full images

256 × 256 resolution full images

256 × 256 resolution upper and lower region images

T-SNE algorithm results for tombstoning class synthesized images

512 × 512 resolution full images

256 × 256 resolution full images

256 × 256 resolution upper and lower region images

Useful linksEdit

• Tero Karras, Timo Aila, Samuli Laine, Jaakko Lehtinen, Progressive Growing of GANs for Improved Quality, Stability, and Variation, February 2018.
• Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, Aaron Courville, Improved Training of Wasserstein GANs, December 2017.
• IPC-A-610E: Acceptability of Electronic Assemblies, a standard developed by IPC, April 2010.